NONINVASIVE DIAGNOSTIC TESTS FOR HELICOBACTER PYLORI INFECTION (REVIEW)
Bibliographic record
Abstract
Jacqueline Pich, PhD, BNurs (Hons I), BSc, is a member of the Cochrane Nursing Care (CNC); and is Lecturer, Faculty of Health, UTS Sydney, Ultimo, New South Wales, Australia. Correspondence to: Jacqueline Pich, PhD, BNurs (Hons I), BSc, Faculty of Health, UTS Sydney, Ultimo, 235 Jones St, Ultimo NSW 2007, Australia ([email protected]). THE OFFICIAL JOURNAL OF THE SOCIETY OF GASTROENTEROLOGY NURSES AND ASSOCIATES, INC. AND THE CANADIAN SOCIETY OF GASTROENTEROLOGY NURSES AND ASSOCIATESDEDICATED TO THE SAFE AND EFFECTIVE PRACTICE OF GASTROENTEROLOGY AND ENDOSCOPY NURSINGThe author declares no conflicts of interest.The Mission of the Cochrane Nursing Care Field (CNCF) is to improve health outcomes through increasing the use of the Cochrane Library and supporting Cochrane's role by providing an evidence base for nurses and related healthcare professionals involved in delivering, leading or researching nursing care. The CNCF produces ‘Cochrane Corner' columns (summaries of recent nursing-care-relevant Cochrane Reviews) that are regularly published in collaborating nursing-care-related journals. Information on the processes this Field has developed can be accessed at: http://cncf.cochrane.org/evidence-transfer-program-review-summaries
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".